Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy -medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distance between the parameters estimated by an INGARCH model. We show how the proposed clustering method works by clustering football teams according to the number of scored goals.

INGARCH-based fuzzy clustering of count time series with a football application / Cerqueti, Roy; D'Urso, Pierpaolo; De Giovanni, Livia; Mattera, Raffaele; Vitale, Vincenzina. - In: MACHINE LEARNING WITH APPLICATIONS. - ISSN 2666-8270. - (2022).

INGARCH-based fuzzy clustering of count time series with a football application

Roy Cerqueti;Pierpaolo D’Urso;Raffaele Mattera;Vincenzina Vitale.
2022

Abstract

Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy -medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distance between the parameters estimated by an INGARCH model. We show how the proposed clustering method works by clustering football teams according to the number of scored goals.
2022
Fuzzy C-medoids; INGARCH; Poisson distribution; Sport analytics
01 Pubblicazione su rivista::01a Articolo in rivista
INGARCH-based fuzzy clustering of count time series with a football application / Cerqueti, Roy; D'Urso, Pierpaolo; De Giovanni, Livia; Mattera, Raffaele; Vitale, Vincenzina. - In: MACHINE LEARNING WITH APPLICATIONS. - ISSN 2666-8270. - (2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1661182
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